Keynotes and Invited Talks

2022

  • Howard A. “Multifidelity Deep Operator Networks,” Workshop on Multi-fidelity DeepONets at Brown University Applied Mathematics CRUNCH Seminar. May 6, 2022. Virtual. (Invited Talk)

  • Howard A. "High performance computing for multiphase flows," 2022 HPC Parallel Programming Workshop, Lehigh University, Bethlehem, PA , June 28, 2022. Virtual. (Invited Talk)   

  • Howard A. “Multifidelity Machine Learning Methods,” CMIT Seminar, University of Liverpool, Liverpool, UK, November 2022. (Invited Talk)

  • Martin M. “Force Coupling Method for Particle-Laden Flows and Recent Applications," University of California, Irvine, Department of Mechanical and Aerospace Engineering Symposium in Honor of Professor Said Elghobashi, September 16, 2022, Irvine, California. (Invited Talk) 

2021

  • Atzberger PJ. “Stochastic Immersed Boundary Methods,” Courant Institute, New York University, New York, NY, April 2021. 
  • Atzberger PJ. “Machine Learning for investigating dynamics of physical systems," PhILMs, DOE, May 2021. 
  • Bochev P. “Hybrid analytic-numerical compact models for radiation-induced photocurrent effects,” A symposium in honor of Jackie Chen’s selection as a 2020 DOE Office of Science Distinguished Scientist Fellow, Sandia National Laboratories, May 26, 2021. 
  • Daskalakis C. “Equilibrium Computation, GANs and the foundations of Deep Learning,” National Technical University of Athens, Greece, January 2021. (Invited Talk)
  • Daskalakis C. “Equilibrium Computation, GANs and the foundations of Deep Learning,” Virtual Seminar Series on Games, Decisions and Networks, January 2021. (Invited Talk).
  • Daskalakis C. “Equilibrium Computation and the foundations of Deep Learning,” AAAI Workshop on Reinforcement Learning in Games, February 2021. (Invited Talk).
  • Daskalakis C. “Three ways Machine Learning fails and what to do about them,” NYIT School of Architecture and Design, February 2021. (Public Lecture).
  • Daskalakis C. “Equilibrium Computation and the Foundations of Deep Learning,” 32nd International Conference on Algorithmic Learning Theory (ALT), March 2021. (Plenary Talk).
  • Daskalakis C. “How AI fails, and why it matters,” Greek Scientists Society Symposium, March 2021. (Public Lecture).
  • Daskalakis C. “The Revolution of Tomorrow and the moral implications of Artificial Intelligence,” Hellenic Innovation Network and Greek Consulate in Boston webinar, March 2021. (Panel Discussion).
  • Daskalakis C. “How does Artificial Intelligence fail and what can we do about it?” Athens Science Virtual Festival, April 2021. (Public Lecture).
  • Daskalakis C. “Robust (ML + MD) = Learned Mechanisms,” Google Market Algorithms Workshop, May 2021. (Invited Talk).
  • Daskalakis C. “How Long Until Truly Intelligent Machines?” University of Crete, May 2021. (Public Lecture).
  • Daskalakis C. “Equilibrium Computation and the Foundations of Deep Learning,” University of Washington, May 2021. (Invited Talk).
  • Daskalakis C. “From von Neumann to Machine Learning: Equilibrium Computation and the Foundations of Deep Learning,” John von Neumann Lecture, University of Zurich and ETH, June 2021. (Public Talk).
  • Daskalakis C. “Equilibrium Computation and Deep Learning,” CVPR conference, June 2021. (Keynote Talk).
  • Daskalakis C. “Min-Max Optimization: from von Neumann to Deep Learning, Nash and Wilson”, Stony Brook Game Theory Festival, July 2021. (Plenary Talk).
  • Daskalakis C. “Min-Max Optimization: from von Neumann to Deep Learning,” Conference on Research on Economic Theory and Econometrics, July 2021, Naxos, Greece. (Plenary Talk).
  • Daskalakis C. “Equilibrium Computation and Machine Learning,” Congress of the Game Theory Society, July 2021. (Semi-Plenary Talk).
  • Daskalakis C. “Min-Max Optimization: from von Neumann to Deep Learning,” Symposium on Fundamentals of Computation Theory, September 2021. (Plenary Talk).
  • D’Elia M. “Challenges in nonlocal modeling: nonlocal boundary conditions and nonlocal interfaces,” WCCMECCOMAS 2020, January 11-15, 2021. 
  • D’Elia M. “Data driven learning of nonlocal models: from MD to continuum mechanics,” NM Machine Learning in Material Science Symposium, February 23, 2021. 
  • D’Elia M. “A General Framework for Nonlocal Domain Decomposition,” SIAM Computational Science and Engineering Conference, March 2021. (Invited Talk).
  • D’Elia M. “Data Driven Learning of Nonlocal Models,” Computing and Mathematical Science Colloquium at the California Institute of Technology, March 10, 2021. (Invited Talk).
  • D’Elia M. “Data Driven Learning of Nonlocal Models: from High Fidelity Simulations to Constitutive Laws,” AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, March 22-24, 2021. (Invited Talk).
  • D’Elia M. “Data Driven Learning of Nonlocal Models,” CNA seminar at Carnegie Mellon University, March 16, 2021. (Invited Talk).
  • D’Elia M. “Data Driven Learning of Nonlocal Models,” Mathematics Department Colloquium at Florida State University, March 26, 2021. (Invited Talk).
  • D’Elia M. “Data driven learning of nonlocal models,” The 50th John H. Barrett Memorial Lectures, May 17-19, 2021. (Keynote).
  • D’Elia M. “A new variable-order fractional Laplacian,” SIAM MS 21, May 17-28, 2021. 
  • D’Elia M. “Addressing micro-scale interfaces via nonlocal models using machine learning,” Coupled Problems 21, June 14-16, 2021. 
  • D’Elia M. “Data driven learning of nonlocal models,” ALOP Workshop, Nonlocal Models: Analysis, Optimization, and Implementation, July 12-14, 2021. (Plenary).
  • D’Elia M. “A unified theory of fractional and nonlocal calculus,” INdAM workshop on Fractional Differential Equations: Modeling, Discretization, and Numerical Solvers, July 12-14, 2021. (Plenary)
  • D’Elia M. "Nonlocal Model Learning: from High-fidelity Simulations to Nonlocal Constitutive Laws,” ALOP Workshop on nonlocal models. July 12, 2021, Trier, Germany. (Plenary talk)
  • D’Elia M. “A Unified Theory of Fractional and Nonlocal Calculus and its Consequences on Nonlocal Model Discovery," INdAM Workshop on Fractional Differential Equations. July 13, 2021, Rome, Italy. (Plenary talk)
  • D’Elia M. “Being a mathematician at a National Laboratory,” REU/RET Panel on Careers in Data Science at the Emory University, July 19, 2021. (Plenary).
  • D’Elia M. “Data driven learning of nonlocal models,” SIAM AN 21. July 22, 2021. 
  • D’Elia M. “Data driven learning of nonlocal models,” 16th U.S. National Congress on Computational Mechanics. July 28, 2021. 
  • D’Elia M. “Data driven learning of nonlocal models,” DDPS Seminar at Lawrence Livermore National Laboratory, July 30, 2021. 
  • D’Elia M. “Data-driven learning of nonlocal models: bridging scales with nonlocality,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, September 2021. 
  • D’Elia M. “Data-driven learning of nonlocal models: bridging scales with nonlocality,” Machine learning in heterogeneous porous materials, AmeriMech Symposium Series, October 4-6, 2021. 
  • D’Elia M. "Recent advances in nonlocal modeling and learning,” University of Whuan. October 26, 2021. (Invited Talk)
  • Gulian M. “Data-driven learning of nonlocal physics from high-fidelity synthetic data,” CONFERENCIA IFIP TC7 2021, August 30, 2021. 
  • Gulian M. “Analysis of Anisotropic Nonlocal Diffusion Models: Well-posedness of Fractional Problems for Anomalous Transport,” SIAM MS 21, May 26, 2021. 
  • Gulian M. “A block coordinate descent optimizer for classification problems exploiting convexity,” AAAI-MLPS, March 3, 2021. 
  • Gulian M. “Robust architectures, initialization, and training for deep neural networks via the adaptive basis interpretation,” SIAM SEAS, September 18, 2021. 
  • Howard A. “Nonlocal models for modeling multiphase fluids,” Arizona State University, Tempe, AZ, 2021.
  • Howard A. “Nonlocal models for modeling multiphase fluids,” San Diego State University, San Diego, CA, 2021. 
  • Howard A. “Nonlocal models for modeling multiphase fluids,” University of Washington, Seattle, WA, 2021.
  • Howard A. “Two multifidelity approaches for machine learning,” RAMSES: Reduced order models; Approximation theory, Machine Learning; Surrogates, Emulators and Simulators, Trieste Italy (online), December 2021. (Invited Talk).
  • Karniadakis GE. "“Physics-Informed Neural Networks PINNs and DeepOnet: Theory and Applications,” Siemens Inc., 2021.
  • Karniadakis GE. "“Physics-Informed Neural Networks PINNs and DeepOnet: Theory and Applications,” Hitachi Inc., 2021.
  • Karniadakis GE. "“Physics-Informed Neural Networks PINNs and DeepOnet: Theory and Applications,” Bosch Inc., 2021.
  • Karniadakis GE. "“Physics-Informed Neural Networks PINNs and DeepOnet: Theory and Applications,” University of Cambridge., 2021.
  • Karniadakis GE. "“Physics-Informed Neural Networks PINNs and DeepOnet: Theory and Applications,” AMD Inc., 2021.
  • Karniadakis GE. "“Physics-Informed Neural Networks PINNs and DeepOnet: Theory and Applications,” 10th Workshop on Parallel-in-Time Integration, 2021. (Plenary Talk).
  • Parks M. “nPINNS: Nonlocal Physics-Informed Neural Networks,” One Nonlocal World, January 23, 2021.
  • Parks, M. “nPINNS: Nonlocal Physics-Informed Neural Networks,” SIAM Computational Science and Engineering Conference, March 2021. (Invited Talk). 
  • Parks M. “Computational Aspects of Nonlocal Models,” Center for Nonlinear Analysis, Department of Mathematical Sciences MCS, Carnegie Mellon University, April 13, 2021.
  • Parks M. "“nPINNS: Nonlocal Physics-Informed Neural Networks,” 16th U.S. National Congress on Computational Mechanics, Chicago, IL, July 29, 2021. (Invited Talk).
  • Parks M. “On Neumann-type Boundary Conditions for Nonlocal Models,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET), San Diego, California, September 2021. (Invited Talk).
  • Perego M. “Modeling land ice with deep operator networks,” SIAM Southeastern Atlantic Section Conference. Sept 19, 2021. 
  • Patel R. “Control volume PINNs: a method for solving inverse problems with hyperbolic PDEs,” Brown University CRUNCH Seminar, January 2021. 
  • Patel R. “A Physics-Informed Operator Regression Framework for Extracting Data-Driven Continuum Models,” SIAM Conference on Computational Science and Engineering, March 2021. 
  • Patel R. “Modal operator regression for extracting nonlocal continuum models,” 16th U.S. National Congress on Computational Mechanics, July 2021. 
  • Stinis, P. “Machine-learning Enhanced Perturbative Renormalization,” SIAM Computational Science and Engineering Conference, March 1-5, 2021. (Invited Talk).
  • Stinis, P. “Machine-learning enhanced perturbative renormalization,” SIAM Conference on Applications of Dynamical Systems (DS21), May 23-27, 2021. (Invited Talk).
  • Stinis, P. “A spectral approach for time-dependent PDE using machine-learned basis functions.” University of Pennsylvania, Applied Mathematics Seminar. December 2021. (Invited Talk)
  • Trask N. “Designing convergent and structure preserving architectures for SciML,” UTEP Department Webinar, February 26, 2021.
  • Trask N. “A data-driven exterior calculus for model discovery,” SIAM CSE, March 1, 2021. 
  • Trask N. “Designing convergent and structure preserving architectures for SciML,” One World ML virtual webinar, March 3, 2021.
  • Trask N. “Physics-informed ML tutorial for Northwestern engineering,” Northwestern Engineering colloquium, March 5, 2021
  • Trask N. “A data-driven exterior calculus for model discovery,” USACM UQ Webinar, March 18, 2021.
  • Trask N. “Making physics-informed ML work,” Los Alamos invited machine learning webinar, March 17, 2021.
  • Trask N. “Partition of unity networks: deep hp-approximation,” AAAI MLPS virtual meeting, March 17, 2021.
  • Trask N. “Structure preservation and mathematical foundations for scientific machine learning,” CIS External Review, March 24, 2021
  • Trask N. “Structure preserving architectures for SciML,” CRUNCH webinar Brown University, June 7, 2021.
  • Trask N. “Structure preserving machine learning for high-consequence engineering and science applications,” New Research Ideas Forum (SNL), June 17, 2021.
  • Trask N. “A data-driven exterior calculus for model discovery,” USACM, July 27, 2021. 
  • Trask N. “Discovery of structure-preserving finite element spaces for multiscale,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, September 27, 2021.
  • Trask N. “A data-driven exterior calculus for model discovery,” RPI engineering webinar, September 20, 2021.
  • Valiant G. “Charting the Landscape of Memory/Data Tradeoffs in Continuous Optimization: A Survey of Open Problems,” Simons Institute for Theory of Computing, workshop on Rigorous Evidence for Information- Computation Trade-offs, September, 2021.
  • Valiant G. “Estimation and Learning Beyond the IID Setting,” Workshop MHC2020: Mixtures, Hidden Markov Models and Clustering, June, 2021.
  • Valiant G. “Statistical Challenges in the Federated Setting,” New Problems and Perspectives on Sampling, Learning, and Memory, April, 2021

2020

  • Atzberger P.J. “Geometric Approaches for Machine Learning in the Sciences and Engineering,” University of California, Davis, May 2020. (Invited Talk).
  • Bochev P.B. "What does a computational scientist do at a national lab," Casper College, November 19, 2020. (Invited Talk).
  • Daskalakis, C. “Statistical Inference from Dependent Observations,” National Technical University of Athens, Athens, Greece, January 2020. (Invited Talk).
  • Daskalakis, C. “Statistical Inference from Dependent Observations,” Institute for Advanced Studies Computer Science/Discrete Mathematics Seminar, Princeton, NJ, March 2020. (Invited Talk).
  • Daskalakis, C. “Min-Max Optimization and Deep Learning,” Institute for Advanced Studies Special Year in Optimization, Statistics, and Theoretical Machine Learning Seminar, Princeton, NJ, March 2020. (Invited Talk).
  • Daskalakis, C. “Robust Learning from Censored Data,” MIT-Microsoft Research Trustworthy and Robust AI Collaboration Workshop, Cambridge, MA, June 2020. (Invited Talk).
  • Daskalakis, C. “The Complexity of Min-Max Optimization,” Universit´e de Montreal Machine Learning-Optimization Seminar, Montreal, Canada, July 2020. (Invited Talk).
  • Daskalakis, C. “Learning from Biased Data,” MIT Brains, Minds, and Machines Summer Course, Cambridge, MA, August 2020. (Invited Lecture).
  • Daskalakis, C. “How does Machine Learning fail, and what to do about it?,” ERC organized session on “Artificial Intelligence: A blessing or a threat for society?” at EuroScience Open Forum (ESOF), Trieste, Italy, September 2020. (Invited Talk and Panel).
  • D’Elia M. “Nonlocal models in computational Science and Engineering,” University of New Mexico, Albuquerque, NM, February 2020. (Invited Lecture)
  • D’Elia M. "Nonlocal Models in Computational Science and Engineering," GA Scientific Computing Symposium, Emory University, Atlanta, GA, February 29, 2020. (Invited Talk).
  • D’Elia M, M Gulian, G Pang, M Parks, and G Karniadakis. "A Unified Theoretical and Computational Nonlocal Framework: Generalized Nonlocal Calculus and Physics-Informed Neural Networks," Recent Progress in Nonlocal Modeling, Analysis and Computation, Beijing, China, June 16, 2020. (Invited Talk).
  • D’Elia, M. “A Unified Theory of Fractional and Nonlocal Vector Calculus,” Brown University, August 2020. (Invited Lecture)
  • D’Elia, M. A unified theoretical and computational nonlocal framework: Generalized vector calculus and machine-learned nonlocal models," CMAI (Center for Mathematics and Artificial Intelligence), George Mason University, Fairfax, VA, August 7, 2020. (Invited Talk).
  • D’Elia M. “A unified theoretical and computational nonlocal framework: generalized vector calculus and machine-learnt nonlocal models,” SIAM TX-LA Section. October 17-18, 2020. 
  • D’Elia M. “A unified, data-driven framework for the identification of nonlocal models: ALGORITHMS & APPLICATIONS,” Engineering Sciences Seminar at Sandia National Laboratories. December 10, 2020. Virtual.
  • D’Elia M., “A unified theoretical and computational nonlocal framework,” Mathematics Department Colloquium at MODEMAT, Ecuador. December 15, 2020. 
  • He, Q. “Machine Learning Enhanced Computational Mechanics,” SE Special Seminar in Computational Mechanics, Department of Structural Engineering at University of California San Diego, La Jolla, California, March 2020. (Invited Talk).
  • Patel R. “PDE discovery with machine learning,” University of New Mexico Applied Math Seminar, November 2020. 
  • Stinis, P. “Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning,” SIAM Conference on Mathematics of Data Science (MDS20), Cleveland, Ohio, June 2020. (Invited Talk).
  • Stinis, P. “Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning,” SIAM/CAIMS Annual Meeting (AN20), Toronto, Canada, July 2020. (Invited Talk).
  • Trask, N. “Compatible meshfree discretization,” UIUC civil engineering colloquium, Urbana-Champaign, IL, February 2020. (Invited Talk)
  • Trask N. “A data-driven exterior calculus for model discovery,” Princeton Plasma Physics Laboratory Webinar, November 16, 2020. 
  • Trask N. “ASCR physics-informed machine learning at SNL,” Presentation to DOE Office of AI, December 10, 2020. 
  • Valiant G. "Constrained Learning," Information Theory and Applications (ITA), San Diego, CA, February 2020. (Plenary Session).
  • Valiant G. "Randomly Collected, Worst Case Data," Workshop on Local Algorithms (WOLA), July 2020. (Plenary Talk).
  • Valiant G. “Statistical Challenges in the Federated Setting,” Federated Learning One World Seminar (FLOW), November, 2020.
  • Valiant G. “Worst-Case Analysis for Randomly Collected Data,” University of Wisconsin, Madison, October, 2020. (Invited Talk)

2019

  • Bochev PB “Mimetic meshfree methods or how to be compatible without a mesh,” Conference on Computational Mathematics and Applications, Las Vegas, NV. October 2019. (Invited Talk).
  • Daskalakis C. Open Data Science Conference, Boston, MA, April 2019. (Keynote).
  • Daskalakis C. ACM Summer School on Data Science, Athens, Greece, July 2019. (Keynote).
  • Daskalakis C. Workshop on Algorithms for Learning and Economics, Rhodes, Greece, July 2019. (Invited Talk).
  • Daskalakis C. RANDOM-APPROX Conference, Cambridge, MA, September 2019. (Keynote).
  • Daskalakis C. H2O AI World New York, New York, NY, October 2019. (Keynote).
  • Daskalakis C. Inference on Graphical Models Conference, Columbia University, New York, NY, October 2019. (Invited Talk).
  • D’Elia M. “Nonlocal models in computational Science and Engineering: challenges and applications,” University of California at Berkeley, Berkely, CA, November 2019. (Invited Talk).
  • He Q. “Machine Learning Enhanced Computational Mechanics: Reduced-Order Modeling and Physics-Informed Data-Driven Computing,” Sonny Astani Civil and Environmental Engineering Seminar, University of Southern California, Los Angeles, California, November 2019. (Invited Talk).
  • Karniadakis GE. "Physics-Informed Neural Networks (PINNs)," Machine Learning in Heliophysics, Amsterdam, Netherlands, Sept 16-20, 2019. (Keynote).
  • Karniadakis GE. "Physics-informed neural networks (PINNs) with uncertainty quantification," FrontUQ19: Workshop on Frontiers of Uncertainty Quantification in Fluid Dynamics, Pisa, Italy, Sept 11-13, 2019. (Keynote)
  • Karniadakis GE. "Uncertainty Quantification for Physics Informed Neural Networks," UNCECOMP: International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Crete, Greece, June 24-26, 2019. (Keynote)
  • Karniadakis GE. "Physics-Informed Learning Machines for Physical Systems," CFD IMPACT Conference, D. Dan and Betty Kahn Mechanical Engineering Building Technion – Israel Institute of Technology, Haifa, Israel, July 1, 2019. (Keynote)
  • Karniadakis GE. 22nd Korean SIAM Conference, Seoul, Korea, May 17-18, 2019. (Keynote)
  • Karniadakis GE. "Physics-Informed Neural Networks (PINNs) for solving stochastic and fractional PDEs," Machine Learning for Multiscale Model Reduction Workshop, Harvard University, Cambridge, MA, March 27-29, 2019. (Keynote).
  • Trask N. ”Compatible meshfree discretization,” Tufts university applied mathematics colloquium, Medford, MA, December 2019. (Invited Talk).
  • Xu K, E Darve, and D Huang. "Physics informed machine learning," 15th U.S. National Congress of Computational Mechanics, Austin, TX, July 28-Aug 1, 2019. (Keynote).

2018

  • Daskalakis C. NIPS 2018 Workshop on "Smooth Games Optimization and Machine Learning," Montreal, Canada, December 2018. (Invited Talk).